Prof. Claudia Czado, Ph.D.



Academic Career and Research Areas

The research activities of Prof. Czado (b. 1959) center on the field of statistics and data science. Her focus lies on modeling complex dependencies including regression effects and time/space structures using vine copula based models. See Vine Copula Models for more details and developments. These allow the construction of high dimensional multivariate distributions for data including different asymmetrical dependencies for each pair of variables. Computer-aided processes are developed/optimized for selection, estimation and adaptation to complex data structures. Applications can be found in finance and insurance as well as in engineering, earth and life sciences. A number of cooperation agreements with various international scientists and industry representatives are in place. In 2019 Prof. Czado has published a text book on analyzing dependent data with vine copulas.

After studying in Göttingen, Prof. Czado received her doctorate from Cornell University in the field of Operations Research and Industrial Engineering in 1989. She then became assistant professor and, in 1995, associate professor at York University, Toronto. In 1998, she was appointed to a professorship position in Applied Mathematical Statistics at TUM. She is the co-founder/coordinator of the “Global Challenges for Women in Math Science” young scientists program at TUM and since 1998 has held the position of (acting) women’s representative for the department. 


  • Fulbright Travel Grant for Senior Scientists (2001)
  • Mathematical Sciences Institute Fellowship, Cornell University (1986 - 1987)
  • Graduate School Summer Fellowship, Cornell University (1988)
  • Graduate School Summer Fellowship, Cornell University (1987)
  • Graduate Exchange Fellowship, Georg-August Universität/Cornell University (1982 - 1983)

Czado C: “Analyzing dependent data with vine copulas“. Lecture Notes in Statistics Vol. 222, Springer. 2019. (261 pages)


Müller D and Czado C: “Representing sparse Gaussian DAGs as sparse R-vines allowing for non-Gaussian dependence”. Journal of Computational and Graphical Statistics, 2018, 27(2): 334 -344 


Kraus D and Czado C: “D-vine copula based quantile regression”. Computational Statistics & Data Analysis. 2017; 110: 1-18.


Dissmann J, Brechmann EC, Czado C and Kurowicka D: “Selecting and estimating regular vine copulae and application to financial returns”. Computational Statistics & Data Analysis, 2013, 59: 2–69. 


Aas K, Czado C, Frigessi A and Bakken H: “Pair-copula constructions of multiple dependence“. Insurance: Mathematics and Economics, 2009, 44(2):182–198.